Systems and methods for offering carbon offset rewards that correspond to users

ABSTRACT

Method and system for offering carbon offset rewards corresponding to users. For example, the method includes detecting actions attributable to a user, determining a level of mindful driving and additional characteristics of the user based upon the attributable actions, determining the user&#39;s quality based upon the level of mindful driving and additional characteristics, determining a level of carbon offset reward based upon the user&#39;s quality, and offering an amount of carbon offset reward to the user based upon the level of carbon offset reward, where the amount of carbon offset reward includes a first amount for planting a first set of trees at a first time and a second amount for planting a second set of trees at a second time with the first time preceding the second time by a time duration that is shorter than or equal to the lifespan of each of the first set of trees.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/000,874, filed Mar. 27, 2020, incorporated by reference hereinfor all purposes.

International PCT Application No. PCT/US21/18233, titled “System andMethods for Providing Renewing Carbon Offsets” is incorporated byreference herein for all purposes.

The following five applications, including this one, are being filedconcurrently and the other four are hereby incorporated by reference intheir entirety for all purposes:

1. International PCT application Ser. No.______, titled “Systems andMethods for Offering Carbon Offset Rewards that Correspond to Users”(Attorney Docket Number BOL-00007A-PCT);

2. International PCT application Ser. No.______, titled “Systems andMethods for Providing Multiple Carbon Offset Sources” (Attorney DocketNumber BOL-00007B-PCT);

3. International PCT application Ser. No.______, titled “Systems andMethods for Generating Tree Imagery” (Attorney Docket NumberBOL-00007G-PCT);

4. International PCT application Ser. No.______, titled “Systems andMethods for Validating Planting of Trees” (Attorney Docket NumberBOL-00007H-PCT); and

5. International PCT application Ser. No.______, titled “Systems andMethods for Providing Renewing Carbon Offsets for a User Driving Period”(Attorney Docket Number BOL-00007J-PCT).

FIELD OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to offeringcarbon offset rewards that correspond to a user. More particularly,certain embodiments of the present disclosure provide methods andsystems for offering the carbon offset rewards to the user based uponthe user's driving behavior and other user characteristics. Merely byway of example, the present disclosure has been applied to offeringcarbon offset rewards in view of a perceived quality of the user as apotential or existing customer to a business. But it would be recognizedthat the present disclosure has much broader range of applicability.

BACKGROUND OF THE DISCLOSURE

Carbon emissions from vehicles represent a major contributor to climatechange. While new vehicle technologies have been developed to curbcarbon emissions, the continued use of vehicles for privatetransportation will cause the amount of carbon emissions to remain highor even increase. Hence it is highly desirable to develop additionalapproaches such as offering carbon offset rewards to compensate for therelease of these carbon emissions.

BRIEF SUMMARY OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to offeringcarbon offset rewards that correspond to a user. More particularly,certain embodiments of the present disclosure provide methods andsystems for offering the carbon offset rewards to the user based uponthe user's driving behavior and other user characteristics. Merely byway of example, the present disclosure has been applied to offeringcarbon offset rewards in view of a perceived quality of the user as apotential or existing customer to a business. But it would be recognizedthat the present disclosure has much broader range of applicability.

According to certain embodiments, a method for offering a carbon offsetreward corresponding to a user includes detecting one or more actionsattributable to the user. Also, the method includes determining aplurality of characteristics related to the user based at least in partupon the one or more actions attributable to the user including a levelof mindful driving of the user and one or more additionalcharacteristics of the user. Additionally, the method includesprocessing information associated with the level of mindful driving ofthe user and the one or more additional characteristics of the user, anddetermining a quality of the user as a perspective or existing customerbased at least in part upon the level of mindful driving of the user andthe one or more additional characteristics of the user. Further, themethod includes determining a level of carbon offset reward based atleast in part upon the quality of the user as the perspective orexisting customer. Moreover, the method includes offering an amount ofcarbon offset reward to the user based at least in part upon the levelof carbon offset reward. The amount of carbon offset reward includes afirst amount for planting one or more first trees at a first time and asecond amount for planting one or more second trees at a second time.The first time precedes the second time by a time duration that isshorter than or equal to a lifespan of each of the one or more firsttrees.

According to some embodiments, a computing device for offering a carbonoffset reward corresponding to a user includes one or more processorsand a memory that stores instructions for execution by the one or moreprocessors. The instructions, when executed, cause the one or moreprocessors to detect one or more actions attributable to the user. Also,the instructions, when executed, cause the one or more processors toanalyze the driving data to determine a plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user including a level of mindful driving of theuser and one or more additional characteristics of the user.Additionally, the instructions, when executed, cause the one or moreprocessors to process information associated with the level of mindfuldriving of the user and the one or more additional characteristics ofthe user, and determine a quality of the user as a perspective orexisting customer based at least in part upon the level of mindfuldriving of the user and the one or more additional characteristics ofthe user. Further, the instructions, when executed, cause the one ormore processors to determine a level of carbon offset reward based atleast in part upon the quality of the user as the perspective orexisting customer. Moreover, the instructions, when executed, cause theone or more processors to offer an amount of carbon offset reward to theuser based at least in part upon the level of carbon offset reward. Theamount of carbon offset reward includes a first amount for planting oneor more first trees at a first time and a second amount for planting oneor more second trees at a second time. The first time precedes thesecond time by a time duration that is shorter than or equal to alifespan of each of the one or more first trees.

According to certain embodiments, a non-transitory computer-readablemedium stores instructions for offering a carbon offset rewardcorresponding to a user. The instructions are executed by one or moreprocessors of a computing device. The non-transitory computer-readablemedium includes instructions to detect one or more actions attributableto the user. Also, the non-transitory computer-readable medium includesinstructions to determine a plurality of characteristics related to theuser based at least in part upon the one or more actions attributable tothe user including a level of mindful driving of the user and one ormore additional characteristics of the user. Additionally, thenon-transitory computer-readable medium includes instructions to processinformation associated with the level of mindful driving of the user andthe one or more additional characteristics of the user, and to determinea quality of the user as a perspective or existing customer based atleast in part upon the level of mindful driving of the user and the oneor more additional characteristics of the user. Further, thenon-transitory computer-readable medium includes instructions todetermine a level of carbon offset reward based at least in part uponthe quality of the user as the perspective or existing customer.Moreover, the non-transitory computer-readable medium includesinstructions to offer an amount of carbon offset reward to the userbased at least in part upon the level of carbon offset reward. Theamount of carbon offset reward includes a first amount for planting oneor more first trees at a first time and a second amount for planting oneor more second trees at a second time. The first time precedes thesecond time by a time duration that is shorter than or equal to alifespan of each of the one or more first trees.

Depending upon the embodiment, one or more benefits may be achieved.These benefits and various additional objects, features and advantagesof the present disclosure can be fully appreciated with reference to thedetailed description and accompanying drawings that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A and FIG. 1B are a simplified method for offering carbon offsetrewards that correspond to a user according to certain embodiments ofthe present disclosure.

FIG. 2 is a simplified system for offering carbon offset rewards thatcorrespond to a user according to some embodiments of the presentdisclosure.

FIG. 3 is a simplified computing device for offering carbon offsetrewards that correspond to a user according to certain embodiments ofthe present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Some embodiments of the present disclosure are directed to offeringcarbon offset rewards that correspond to a user. More particularly,certain embodiments of the present disclosure provide methods andsystems for offering the carbon offset rewards to the user based uponthe user's driving behavior and other user characteristics. Merely byway of example, the present disclosure has been applied to offeringcarbon offset rewards in view of a perceived quality of the user as apotential or existing customer to a business. But it would be recognizedthat the present disclosure has much broader range of applicability.

I. One or More Methods for Offering Carbon Offset Rewards thatCorrespond to a User According to Certain Embodiments

FIG. 1A and FIG. 1B are a simplified method for offering carbon offsetrewards that correspond to a user according to certain embodiments ofthe present disclosure. The diagrams are merely examples, which shouldnot unduly limit the scope of the claims. One of ordinary skill in theart would recognize many variations, alternatives, and modifications.The method 100 includes process 110 for detecting actions attributableto the user, process 115 for determining a plurality of characteristicsof the user, process 120 for processing information associated with theplurality of characteristics, process 125 for determining a quality ofthe user, process 130 for determining a level of carbon offset reward,process 135 for offering an amount of carbon offset reward including afirst amount for planting first trees at a first time and a secondamount for planting second trees at a second time, process 140 for usingthe first amount for planting the first trees at the first time, process145 for investing the second amount to become a third amount, process150 for using a first part of the third amount for planting the secondtrees at the second time, and process 155 for investing a second part ofthe third amount for planting third trees at a third time. Although theabove has been shown using a selected group of processes for the method,there can be many alternatives, modifications, and variations. Forexample, some of the processes may be expanded and/or combined. Otherprocesses may be inserted to those noted above. Depending upon theembodiment, the sequence of processes may be interchanged with othersreplaced. For example, some or all processes of the method are performedby a computing device or a processor directed by instructions stored inmemory. As an example, some or all processes of the method are performedaccording to instructions stored in a non-transitory computer-readablemedium.

At the process 110, one or more actions attributable to the user aredetected according to some embodiments. In various embodiments, the oneor more actions relate to any number of everyday activities carried outby the user. In some embodiments, the one or more actions areautomatically logged by a computing device (e.g., mobile device) thatthe user is carrying or using. In certain embodiments, the one or moreactions are manually recorded by the user in the computing device. Forexample, the one or more actions relate to the user making a vehicletrip. As an example, the one or more actions relate to the userinteracting with a device. For example, the one or more actions relateto the user completing a certain business task such as completing aquote from an insurance company.

At the process 115, the plurality of characteristics related to the userare determined based at least in part upon the one or more actionsattributable to the user according to certain embodiments. For example,the plurality of characteristics include a level of mindful driving ofthe user and one or more additional characteristics of the user.

According to various embodiments, the one or more actions attributableto the user include the user making one or more vehicle trips. In someembodiments, driving data associated with the one or more vehicle tripsare analyzed to determine the level of mindful driving of the userand/or the one or more additional characteristics of the user. Forexample, the driving data indicate how the user drives, such as howfrequently the user drives, type of vehicle that the user drives (e.g.,model/year/make), type of maneuvers that the user makes while driving(e.g., hard cornering, hard braking, sudden acceleration, smoothacceleration, slowing before turning, etc.), types of dangerous drivingevents (e.g., cell phone usage while driving, eating while driving,falling asleep while driving, etc.), types of safe driving events (e.g.,maintaining safe following distance, turning on headlights, observingtraffic lights, yielding to pedestrians, obeying speed limits, etc.),and/or number of reported accidents/collisions.

In certain embodiments, the driving data are collected from one or moresensors associated with a vehicle operated by the user. For example, theone or more sensors include any type and number of accelerometers,gyroscopes, magnetometers, barometers, location sensors (e.g., GPSsensors), tilt sensors, yaw rate sensors, speedometers, brake sensors,airbag deployment sensors, headlight sensors, steering angle sensors,gear position sensors, proximity detectors, and/or any other suitablesensors that measure vehicle state and/or operation. In someembodiments, the one or more sensors are part of or located in thevehicle. In certain embodiments, the one or more sensors are part of amobile device connected to the vehicle while the vehicle is inoperation. According to some embodiments, the driving data are collectedcontinuously or at predetermined time intervals. According to certainembodiments, the driving data are collected based on a triggering event.For example, the driving data are collected when each sensor hasacquired a threshold amount of sensor measurements.

In some embodiments, the level of mindful driving of the user isdetermined based at least in part upon the analyzed driving data. Forexample, a high level of mindful driving is determined if analysis ofthe driving data shows that the user always exercises safe driving withno reported accidents/collisions. As an example, a medium level ofmindful driving is determined if analysis of the driving data shows thatthe user exercises safe driving but has one or two reportedaccidents/collisions. For example, a low level of mindful driving isdetermined if analysis of the driving data shows that the user exercisesreckless driving with multiple reported accidents/collisions. In someembodiments, the level of mindful driving is represented as a numericalscore. For example, a score of 0-40 represents a low level of mindfuldriving, a score of 40-85 represents a medium level of mindful driving,and a score of 85+ represents a high level of mindful driving. Incertain embodiments, mindful driving is used as a measure thatincorporates collision risk, gas consumption, and/or other factorsrelated to driving. In some embodiments, the level of mindful driving isproxied by claims data, mileage data, and/or other data related tomindful driving behaviors.

In certain embodiments, the one or more additional characteristics ofthe user are determined based at least in part upon the analyzed drivingdata. For example, the one or more additional characteristics mayindicate that the user has reduced a number of the one or more vehicletrips when analysis of the driving data shows less driving on the partof the user. As an example, the one or more additional characteristicsmay indicate that the user has improved his/her fuel consumption whenanalysis of the driving data shows the user has switched to driving amore fuel-efficient vehicle.

According to some embodiments, the one or more actions attributable tothe user include the user making a trip between a particular pair oforigination and destination points. For example, the trip may be acommute between the user's home and the user's workplace. In certainembodiments, trip data associated with the trip are analyzed todetermine the one or more additional characteristics of the user. Forexample, the one or more additional characteristics may indicate thatthe user is using an alternate form of transportation to make the tripwhen analysis of the trip data shows the user walking, biking, and/orriding public transit to travel between the particular pair oforigination and destination points.

According to certain embodiments, the one or more actions attributableto the user include the user interacting with a mobile device. In someembodiments, mobile device data associated with the user interactingwith the mobile device are analyzed to determine the one or moreadditional characteristics of the user. For example, the one or moreadditional characteristics may indicate that the user is using anapplication installed on the mobile device for a certain number of timeswhen analysis of the mobile device data shows the user repeatedlyaccessing the application on the mobile device during a specifiedperiod. As an example, the one or more additional characteristics mayindicate that the user is referring a friend to use the application whenanalysis of the mobile device data shows the user texting a link to thefriend and receiving a notice that the friend has downloaded theapplication on the friend's mobile device using the link.

In various embodiments, relevant data (e.g., driving data, trip data,mobile device data) are provided to a model (e.g., a machine learningmodel, a statistical model, etc.) to determine the plurality ofcharacteristics related to the user such as the level of mindful drivingof the user and the one or more additional characteristics of the user.In certain embodiments, the model is an artificial neural network (e.g.,a convolutional neural network, a recurrent neural network, a modularneural network, etc.). In some embodiments, the model has been trained,and the trained model possesses existing knowledge of which features inthe relevant data are desirable or useful in determining the pluralityof characteristics. For example, determining the plurality ofcharacteristics involves that the trained model analyzes the relevantdata based upon the existing knowledge. As an example, analyzing therelevant data includes various tasks such as performing featureextractions, applying pattern recognition, and/or other suitable tasks.In certain embodiments, other suitable computational methods (e.g.,decision tree, Bayesian network, finite-state machine, support vectormachine, etc.) may be used to analyze the relevant data and determinethe plurality of characteristics.

At the process 120, information associated with the level of mindfuldriving of the user and the one or more additional characteristics ofthe user are processed according to some embodiments. For example, theinformation associated with the level of mindful driving and the one ormore additional characteristics are formatted to be stored in adatabase. As an example, the information the information associated withthe level of mindful driving and the one or more additionalcharacteristics are used to create a profile of the user along withother user identification data. For example, the information associatedwith the level of mindful driving and the one or more additionalcharacteristics are formatted to be displayed on the user's mobiledevice (e.g., display the level of mindful driving as a score).

At the process 125, the quality of the user as a perspective or existingcustomer is determined based at least in part upon the level of mindfuldriving of the user and the one or more additional characteristics ofthe user according to certain embodiments. In some embodiments, thelevel of mindful driving indicates the user's propensity to be a safedriver. For example, the user will be afforded a greater amountinsurance discount (e.g., reduction in insurance premium, reduction inpremium renewal, etc.) from an insurance company if the level of mindfuldriving is high. As an example, the user will be incentivized to engagein business with the insurance company (e.g., become a new customer orcontinue as a current customer). For example, the quality of the user asa customer to the insurance company will be high as a result.

In certain embodiments, the one or more additional characteristicsindicate the user's propensity to interact with a business (e.g.,insurance company). For example, the quality of the user can berepresented by an expected business value of the user. In variousembodiments, the expected business value of the user is determined basedat least in part upon a probability that the user will become a customerof the business and an estimated revenue from the user if the userbecomes the customer of the business. For example, determining theprobability that the user will become the customer is based at least inpart upon a probability that the user will initiate a quote (e.g.,insurance quote), a probability that the user will complete the quote,and a probability that the user will submit the completed quote. As anexample, determining the estimated revenue from the user if the userbecomes the customer is based at least in part upon an amount of moneythat the user will bring to or spend on the business. For example, theexpected business value of the user is determined based at least in partupon the estimated revenue from the user multiplied by the probabilitythat the user will submit the quote multiplied by the probability thatthe user will complete the quote multiplied by the probability that theuser will even initiate the quote in the first place. According to someembodiments, the estimated revenue from the user and the probabilitiesof initiating, completing, and/or submitting the quote are based atleast in part upon the business needs and/or personalities of the user.

At the process 130, the level of carbon offset reward is determinedbased at least in part upon the quality of the user as the perspectiveor existing customer according to certain embodiments. For example, ahigh value for the quality of the user (e.g., user is perceived to be anexceptional customer) produces a high level of carbon offset reward. Asan example, a lower value for the quality of the user (e.g., user isperceived to be an ordinary customer) results in a low level of carbonoffset reward.

At the process 135, the amount of carbon offset reward is offered to theuser based at least in part upon the level of carbon offset rewardaccording to some embodiments. For example, the amount of carbon offsetreward is used to incentivize the user to engage with a business. Incertain embodiments, the amount of carbon offset reward corresponds toan amount of cost (e.g., money) needed for planting of trees. As anexample, the amount of cost needed for the planting of trees may bewaived for the user if a high level of carbon offset reward isdetermined for the user.

According to various embodiments, the planting of trees is carried outin a renewable fashion in which new trees are planted when alreadyplanted trees die. For example, when a tree dies, the carbon stored inthe tree is released back to the atmosphere. As an example, the plantingof a new tree will ensure that the carbon is permanently recaptured andstored in a tree. In some embodiments, the planting of trees isperformed by an organization engaged in carbon emission reductionprojects/programs.

In some embodiments, the amount of carbon offset reward includes thefirst amount for planting one or more first trees at the first time, andthe second amount for planting one or more second trees at the secondtime. For example, the first time precedes the second time by a firsttime duration that is shorter than or equal to a first lifespan of eachof the one or more first trees. In some examples, if a tree has alifespan of 25 years, then a new tree is planted at the 15-year mark toensure that there will always be a tree to store the carbon in theoriginal tree.

At the process 140, the first amount of carbon offset reward is used toplant the one or more first trees at the first time according to certainembodiments. At the process 145, the second amount of carbon offsetreward is invested (e.g., in stocks, mutual funds, savings account,etc.) during the first time duration according to some embodiments. Forexample, the second amount is invested so that it can grow to become athird amount needed for the subsequent planting of new trees at latertimes. In some embodiments, the third amount includes a first part and asecond part.

At the process 150, after the first time duration, the first part of thethird amount is used to plant the one or more second trees at the secondtime according to certain embodiments. At the process 155, the secondpart of the third amount is invested for planting one or more thirdtrees at a third time according to some embodiments. For example, thesecond time precedes the third time by a second time duration that isshorter than or equal to a second lifespan of each of the one or moresecond trees. In certain embodiments, the second part is invested sothat it can grow to become a fourth amount that includes a third partand a fourth part. For example, the third part is used to plant the oneor more third trees at the third time, and the fourth part is againinvested for the planting of additional or future trees (e.g., plantingof one or more fourth trees at a fourth time).

According to various embodiments, the process 135, the process 140, theprocess 145, the process 150, and/or the process 155 are repeatedcontinuously unless interrupted by external instructions so that carbonemissions by the user are effectively captured and stored for apredetermined period of time. For example, the predetermined period oftime is longer than one lifespan of a tree. In some embodiments, theamount of carbon offset reward is always divided into two parts, withone part being used to plant one or more present trees and the otherpart being invested such that additional trees are planted in the futureto replace and/or supplement the one or more present trees. In certainembodiments, the process 135, the process 140, the process 145, theprocess 150, and/or the process 155 are repeated for an infinite numberof times.

II. One or More Systems for Offering Carbon Offset Rewards thatCorrespond to a User According to Certain Embodiments

FIG. 2 is a simplified system for offering carbon offset rewards thatcorrespond to a user according to certain embodiments of the presentdisclosure. This diagram is merely an example, which should not undulylimit the scope of the claims. One of ordinary skill in the art wouldrecognize many variations, alternatives, and modifications. The system200 includes a vehicle system 202, a network 204, and a server 206.Although the above has been shown using a selected group of componentsfor the system, there can be many alternatives, modifications, andvariations. For example, some of the components may be expanded and/orcombined. Other components may be inserted to those noted above.Depending upon the embodiment, the arrangement of components may beinterchanged with others replaced.

In various embodiments, the system 200 is used to implement the method100. According to certain embodiments, the vehicle system 202 includes avehicle 210 and a client device 212 associated with the vehicle 210. Forexample, the client device 212 is an on-board computer embedded orlocated in the vehicle 210. As an example, the client device 212 is amobile device (e.g., a smartphone) that is connected (e.g., via wired orwireless links) to the vehicle 210. As an example, the client device 212includes a processor 216 (e.g., a central processing unit (CPU), agraphics processing unit (GPU)), a memory 218 (e.g., random-accessmemory (RAM), read-only memory (ROM), flash memory), a communicationsunit 220 (e.g., a network transceiver), a display unit 222 (e.g., atouchscreen), and one or more sensors 224 (e.g., an accelerometer, agyroscope, a magnetometer, a barometer, a GPS sensor).

In some embodiments, the vehicle 210 is operated by the user. In certainembodiments, multiple vehicles 210 exist in the system 200 which areoperated by respective users. As an example, during vehicle trips, theone or more sensors 224 monitor the vehicle 210 by collecting dataassociated with various operating parameters of the vehicle, such asspeed, acceleration, braking, location, engine status, fuel level, aswell as other suitable parameters. In certain embodiments, the collecteddata include vehicle telematics data. According to some embodiments, thedata are collected continuously, at predetermined time intervals, and/orbased on a triggering event (e.g., when each sensor has acquired athreshold amount of sensor measurements). In various embodiments, thecollected data represent the driving data in the method 100.

According to certain embodiments, the collected data are stored in thememory 218 before being transmitted to the server 206 using thecommunications unit 220 via the network 204 (e.g., via a local areanetwork (LAN), a wide area network (WAN), the Internet). In someembodiments, the collected data are transmitted directly to the server206 via the network 204. In certain embodiments, the collected data aretransmitted to the server 206 via a third party. For example, a datamonitoring system stores any and all data collected by the one or moresensors 224 and transmits those data to the server 206 via the network204 or a different network.

According to certain embodiments, the server 206 includes a processor230 (e.g., a microprocessor, a microcontroller), a memory 232, acommunications unit 234 (e.g., a network transceiver), and a datastorage 236 (e.g., one or more databases). In some embodiments, theserver 206 is a single server, while in certain embodiments, the server206 includes a plurality of servers with distributed processing. In FIG.2 , the data storage 236 is shown to be part of the server 206. In someembodiments, the data storage 236 is a separate entity coupled to theserver 206 via a network such as the network 204. In certainembodiments, the server 206 includes various software applicationsstored in the memory 232 and executable by the processor 230. Forexample, these software applications include specific programs,routines, or scripts for performing functions associated with the method100. As an example, the software applications include general-purposesoftware applications for data processing, network communication,database management, web server operation, and/or other functionstypically performed by a server.

According to various embodiments, the server 206 receives, via thenetwork 204, the data collected by the one or more sensors 224 using thecommunications unit 234 and stores the data in the data storage 236. Forexample, the server 206 then processes the data to perform one or moreprocesses of the method 100.

According to certain embodiments, any related information determined orgenerated by the method 100 (e.g., level of mindful driving, amount ofcarbon offset reward, planting of current and/or future trees, etc.) aretransmitted back to the client device 212, via the network 204, to beprovided (e.g., displayed) to the user via the display unit 222.

In some embodiments, one or more processes of the method 100 areperformed by the client device 212. For example, the processor 216 ofthe client device 212 processes the data collected by the one or moresensors 224 to perform one or more processes of the method 100.

III. One or More Computing Devices for Offering Carbon Offset Rewardsthat Correspond to a User According to Certain Embodiments

FIG. 3 is a simplified computing device for offering carbon offsetrewards that correspond to a user according to certain embodiments ofthe present disclosure. This diagram is merely an example, which shouldnot unduly limit the scope of the claims. One of ordinary skill in theart would recognize many variations, alternatives, and modifications.The computing device 300 includes a processing unit 304, a memory unit306, an input unit 308, an output unit 310, a communication unit 312,and a storage unit 314. In various embodiments, the computing device 300is configured to be in communication with a user 316 and/or a storagedevice 318. In some embodiments, the computing device 300 is configuredto implement the method 100 of FIG. 1A and/or FIG. 1B. Although theabove has been shown using a selected group of components for thesystem, there can be many alternatives, modifications, and variations.For example, some of the components may be expanded and/or combined.Other components may be inserted to those noted above. Depending uponthe embodiment, the arrangement of components may be interchanged withothers replaced.

In various embodiments, the processing unit 304 is configured forexecuting instructions, such as instructions to implement the method 100of FIG. 1A and/or FIG. 1B. In some embodiments, the executableinstructions are stored in the memory unit 306. In certain embodiments,the processing unit 304 includes one or more processing units (e.g., ina multi-core configuration). In some embodiments, the processing unit304 includes and/or is communicatively coupled to one or more modulesfor implementing the methods and systems described in the presentdisclosure. In certain embodiments, the processing unit 304 isconfigured to execute instructions within one or more operating systems.In some embodiments, upon initiation of a computer-implemented method,one or more instructions is executed during initialization. In certainembodiments, one or more operations is executed to perform one or moreprocesses described herein. In some embodiments, an operation may begeneral or specific to a particular programming language (e.g., C, C++,Java, or other suitable programming languages, etc.).

In various embodiments, the memory unit 306 includes a device allowinginformation, such as executable instructions and/or other data to bestored and retrieved. In some embodiments, the memory unit 306 includesone or more computer readable media. In certain embodiments, the memoryunit 306 includes computer readable instructions for providing a userinterface, such as to the user 316, via the output unit 310. In someembodiments, a user interface includes a web browser and/or a clientapplication. For example, a web browser enables the user 316 to interactwith media and/or other information embedded on a web page and/or awebsite. In certain embodiments, the memory unit 306 includes computerreadable instructions for receiving and processing an input via theinput unit 308. In some embodiments, the memory unit 306 includes RAMsuch as dynamic RAM (DRAM) or static RAM (SRAM), ROM, erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and/or non-volatile RAM (NVRAM).

In various embodiments, the input unit 308 is configured to receiveinput (e.g., from the user 316). In some embodiments, the input unit 308includes a keyboard, a pointing device, a mouse, a stylus, a touchsensitive panel (e.g., a touch pad or touch screen), a gyroscope, anaccelerometer, a position sensor (e.g., GPS sensor), and/or an audioinput device. In certain embodiments, the input unit 308 is configuredto function as both an input unit and an output unit.

In various embodiments, the output unit 310 includes a media output unitconfigured to present information to the user 316. In some embodiments,the output unit 310 includes any component capable of conveyinginformation to the user 316. In certain embodiments, the output unit 310includes an output adapter such as a video adapter and/or an audioadapter. For example, the output unit 310 is operatively coupled to theprocessing unit 304 and/or a visual display device to presentinformation to the user 316 (e.g., a liquid crystal display (LCD), alight emitting diode (LED) display, an organic light emitting diode(OLED) display, a cathode ray tube (CRT) display, a projected display,etc.). As an example, the output unit 310 is operatively coupled to theprocessing unit 304 and/or an audio display device to presentinformation to the user 316 (e.g., a speaker arrangement or headphones).

In various embodiments, the communication unit 312 is configured to becommunicatively coupled to a remote device. In some embodiments, thecommunication unit 312 includes a wired network adapter, a wirelessnetwork adapter, a wireless data transceiver for use with a mobile phonenetwork (e.g., 3G, 4G, 5G, Bluetooth, etc.), and/or other mobile datanetworks. In certain embodiments, other types of short-range orlong-range networks may be used. In some embodiments, the communicationunit 312 is configured to provide email integration for communicatingdata between a server and one or more clients.

In various embodiments, the storage unit 314 is configured to enablecommunication between the computing device 300 and the storage device318. In some embodiments, the storage unit 314 is a storage interface.For example, the storage interface is any component capable of providingthe processing unit 304 with access to the storage device 318. Incertain embodiments, the storage unit 314 includes an advancedtechnology attachment (ATA) adapter, a serial ATA (SATA) adapter, asmall computer system interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any other component capable ofproviding the processing unit 304 with access to the storage device 318.

In various embodiments, the storage device 318 includes anycomputer-operated hardware suitable for storing and/or retrieving data.In certain embodiments, the storage device 318 is integrated in thecomputing device 300. In some embodiments, the storage device 318includes a database such as a local database or a cloud database. Incertain embodiments, the storage device 318 includes one or more harddisk drives. In some embodiments, the storage device 318 is external andis configured to be accessed by a plurality of server systems. Incertain embodiments, the storage device 318 includes multiple storageunits such as hard disks or solid state disks in a redundant array ofinexpensive disks configuration. In some embodiments, the storage device318 includes a storage area network and/or a network attached storagesystem.

IV. Examples of Certain Embodiments of the Present Disclosure

According to certain embodiments, a method for offering a carbon offsetreward corresponding to a user includes detecting one or more actionsattributable to the user. Also, the method includes determining aplurality of characteristics related to the user based at least in partupon the one or more actions attributable to the user including a levelof mindful driving of the user and one or more additionalcharacteristics of the user. Additionally, the method includesprocessing information associated with the level of mindful driving ofthe user and the one or more additional characteristics of the user, anddetermining a quality of the user as a perspective or existing customerbased at least in part upon the level of mindful driving of the user andthe one or more additional characteristics of the user. Further, themethod includes determining a level of carbon offset reward based atleast in part upon the quality of the user as the perspective orexisting customer. Moreover, the method includes offering an amount ofcarbon offset reward to the user based at least in part upon the levelof carbon offset reward. The amount of carbon offset reward includes afirst amount for planting one or more first trees at a first time and asecond amount for planting one or more second trees at a second time.The first time precedes the second time by a time duration that isshorter than or equal to a lifespan of each of the one or more firsttrees. For example, the method is implemented according to at least FIG.1A and/or FIG. 1B.

As an example, the method for offering the carbon offset reward furtherincludes using the first amount for planting the one or more first treesat the first time. During the first time duration, the method includesinvesting the second amount to become a third amount including a firstpart and a second part. After the first time duration, the methodincludes using the first part of the third amount for planting the oneor more second trees at the second time. Moreover, the method includesinvesting the second part of the third amount for planting one or morethird trees at a third time. The second time precedes the third time bya second time duration that is shorter than or equal to a secondlifespan corresponding to each of the one or more second trees. Forexample, the method is implemented according to at least FIG. 1A and/orFIG. 1B.

According to some embodiments, a computing device for offering a carbonoffset reward corresponding to a user includes one or more processorsand a memory that stores instructions for execution by the one or moreprocessors. The instructions, when executed, cause the one or moreprocessors to detect one or more actions attributable to the user. Also,the instructions, when executed, cause the one or more processors toanalyze the driving data to determine a plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user including a level of mindful driving of theuser and one or more additional characteristics of the user.Additionally, the instructions, when executed, cause the one or moreprocessors to process information associated with the level of mindfuldriving of the user and the one or more additional characteristics ofthe user, and determine a quality of the user as a perspective orexisting customer based at least in part upon the level of mindfuldriving of the user and the one or more additional characteristics ofthe user. Further, the instructions, when executed, cause the one ormore processors to determine a level of carbon offset reward based atleast in part upon the quality of the user as the perspective orexisting customer. Moreover, the instructions, when executed, cause theone or more processors to offer an amount of carbon offset reward to theuser based at least in part upon the level of carbon offset reward. Theamount of carbon offset reward includes a first amount for planting oneor more first trees at a first time and a second amount for planting oneor more second trees at a second time. The first time precedes thesecond time by a time duration that is shorter than or equal to alifespan of each of the one or more first trees. For example, thecomputing device is implemented according to at least FIG. 2 and/or FIG.3 .

According to some embodiments, a non-transitory computer-readable mediumstores instructions for offering a carbon offset reward corresponding toa user. The instructions are executed by one or more processors of acomputing device. The non-transitory computer-readable medium includesinstructions to detect one or more actions attributable to the user.Also, the non-transitory computer-readable medium includes instructionsto determine a plurality of characteristics related to the user based atleast in part upon the one or more actions attributable to the userincluding a level of mindful driving of the user and one or moreadditional characteristics of the user. Additionally, the non-transitorycomputer-readable medium includes instructions to process informationassociated with the level of mindful driving of the user and the one ormore additional characteristics of the user, and to determine a qualityof the user as a perspective or existing customer based at least in partupon the level of mindful driving of the user and the one or moreadditional characteristics of the user. Further, the non-transitorycomputer-readable medium includes instructions to determine a level ofcarbon offset reward based at least in part upon the quality of the useras the perspective or existing customer. Moreover, the non-transitorycomputer-readable medium includes instructions to offer an amount ofcarbon offset reward to the user based at least in part upon the levelof carbon offset reward. The amount of carbon offset reward includes afirst amount for planting one or more first trees at a first time and asecond amount for planting one or more second trees at a second time.The first time precedes the second time by a time duration that isshorter than or equal to a lifespan of each of the one or more firsttrees. For example, the non-transitory computer-readable medium isimplemented according to at least FIG. 1A, FIG. 1B, FIG. 2 , and/or FIG.3 .

V. Examples of Machine Learning According to Certain Embodiments

According to some embodiments, a processor or a processing element maybe trained using supervised machine learning and/or unsupervised machinelearning, and the machine learning may employ an artificial neuralnetwork, which, for example, may be a convolutional neural network, arecurrent neural network, a deep learning neural network, areinforcement learning module or program, or a combined learning moduleor program that learns in two or more fields or areas of interest.Machine learning may involve identifying and recognizing patterns inexisting data in order to facilitate making predictions for subsequentdata. Models may be created based upon example inputs in order to makevalid and reliable predictions for novel inputs.

According to certain embodiments, machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as images, object statistics and information, historical estimates,and/or actual repair costs. The machine learning programs may utilizedeep learning algorithms that may be primarily focused on patternrecognition and may be trained after processing multiple examples. Themachine learning programs may include Bayesian Program Learning (BPL),voice recognition and synthesis, image or object recognition, opticalcharacter recognition, and/or natural language processing. The machinelearning programs may also include natural language processing, semanticanalysis, automatic reasoning, and/or other types of machine learning.

According to some embodiments, supervised machine learning techniquesand/or unsupervised machine learning techniques may be used. Insupervised machine learning, a processing element may be provided withexample inputs and their associated outputs and may seek to discover ageneral rule that maps inputs to outputs, so that when subsequent novelinputs are provided the processing element may, based upon thediscovered rule, accurately predict the correct output. In unsupervisedmachine learning, the processing element may need to find its ownstructure in unlabeled example inputs.

VI. Additional Considerations According to Certain Embodiments

For example, some or all components of various embodiments of thepresent disclosure each are, individually and/or in combination with atleast another component, implemented using one or more softwarecomponents, one or more hardware components, and/or one or morecombinations of software and hardware components. As an example, some orall components of various embodiments of the present disclosure eachare, individually and/or in combination with at least another component,implemented in one or more circuits, such as one or more analog circuitsand/or one or more digital circuits. For example, while the embodimentsdescribed above refer to particular features, the scope of the presentdisclosure also includes embodiments having different combinations offeatures and embodiments that do not include all of the describedfeatures. As an example, various embodiments and/or examples of thepresent disclosure can be combined.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform the methods andoperations described herein. Certain implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to perform the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results) maybe stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, EEPROM, Flashmemory, flat files, databases, programming data structures, programmingvariables, IF-THEN (or similar type) statement constructs, applicationprogramming interface). It is noted that data structures describeformats for use in organizing and storing data in databases, programs,memory, or other computer-readable media for use by a computer program.

The systems and methods may be provided on many different types ofcomputer-readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, DVD) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein. The computer components, software modules, functions,data stores and data structures described herein may be connecteddirectly or indirectly to each other in order to allow the flow of dataneeded for their operations. It is also noted that a module or processorincludes a unit of code that performs a software operation, and can beimplemented for example as a subroutine unit of code, or as a softwarefunction unit of code, or as an object (as in an object-orientedparadigm), or as an applet, or in a computer script language, or asanother type of computer code. The software components and/orfunctionality may be located on a single computer or distributed acrossmultiple computers depending upon the situation at hand.

The computing system can include client devices and servers. A clientdevice and server are generally remote from each other and typicallyinteract through a communication network. The relationship of clientdevice and server arises by virtue of computer programs running on therespective computers and having a client device-server relationship toeach other.

This specification contains many specifics for particular embodiments.Certain features that are described in this specification in the contextof separate embodiments can also be implemented in combination in asingle embodiment. Conversely, various features that are described inthe context of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations, one or more features from a combination can in some casesbe removed from the combination, and a combination may, for example, bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Although specific embodiments of the present disclosure have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the present disclosure is notto be limited by the specific illustrated embodiments.

What is claimed is:
 1. A method for offering a carbon offset rewardcorresponding to a user, the method comprising: detecting, by acomputing device, one or more actions attributable to the user;determining, by the computing device; a plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user, the plurality of characteristics including alevel of mindful driving of the user and one or more additionalcharacteristics of the user; processing; by the computing device,information associated with the level of mindful driving of the user andthe one or more additional characteristics of the user; determining, bythe computing device, a quality of the user as a perspective or existingcustomer based at least in part upon the level of mindful driving of theuser and the one or more additional characteristics of the user;determining, by the computing device, a level of carbon offset rewardbased at least in part upon the quality of the user as the perspectiveor existing customer; and offering, by the computing device, an amountof carbon offset reward to the user based at least in part upon thelevel of carbon offset reward; wherein: the amount of carbon offsetreward includes a first amount for planting one or more first trees at afirst time and a second amount for planting one or more second trees ata second time; each of the one or more first trees corresponds to alifespan; the first time precedes the second time by a time duration;and the time duration is shorter than or equal to the lifespan.
 2. Themethod of claim 1, wherein: the one or more actions attributable to theuser include the user making one or more vehicle trips; and thedetermining, by the computing device, the plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user includes: analyzing driving data associatedwith the one or more vehicle trips; and determining the level of mindfuldriving of the user based at least in part upon the driving data.
 3. Themethod of claim 1, wherein: the one or more actions attributable to theuser include the user making one or more vehicle trips; and thedetermining, by the computing device, the plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user includes: analyzing driving data associatedwith the one or more vehicle trips; and determining the one or moreadditional characteristics of the user based at least in part upon thedriving data, the one or more additional characteristics being the userreducing a number of the one or more vehicle trips.
 4. The method ofclaim 1, wherein: the one or more actions attributable to the userinclude the user making a trip between a particular pair of originationand destination points; and the determining, by the computing device,the plurality of characteristics related to the user based at least inpart upon the one or more actions attributable to the user includes:analyzing trip data associated with the trip between the particular pairof origination and destination points; and determining the one or moreadditional characteristics of the user based at least in part upon thetrip data, the one or more additional characteristics being the userusing an alternate form of transportation to make the trip between theparticular pair of origination and destination points.
 5. The method ofclaim 1, wherein: the one or more actions attributable to the userinclude the user interacting with a mobile device; and the determining,by the computing device, the plurality of characteristics related to theuser based at least in part upon the one or more actions attributable tothe user includes: analyzing mobile device data associated with the userinteracting with the mobile device; and determining the one or moreadditional characteristics of the user based at least in part upon themobile device data, the one or more additional characteristics being theuser using an application installed on the mobile device for a certainnumber of times.
 6. The method of claim 1, wherein the one or moreactions attributable to the user include the user interacting with amobile device; and the determining, by the computing device, theplurality of characteristics related to the user based at least in partupon the one or more actions attributable to the user includes:analyzing mobile device data associated with the user interacting withthe mobile device; and determining the one or more additionalcharacteristics of the user based at least in part upon the mobiledevice data, the one or more additional characteristics being the userreferring a friend to use an application installed on the mobile device.7. The method of claim 1, wherein the determining, by the computingdevice, the quality of the user as the perspective or existing customerbased at least in part upon the level of mindful driving of the user andthe one or more additional characteristics of the user includes:determining a probability that the user will become a customer and anestimated revenue from the user if the user becomes the customer; anddetermining an expected business value of the user based at least inpart upon the probability that the user will become the customer and theestimated revenue from the user if the user becomes the customer, theexpected business value of the user indicating the quality of the user.8. A computing device for offering a carbon offset reward correspondingto a user, the computing device comprising: one or more processors; anda memory storing instructions that, when executed by the one or moreprocessors, cause the one or more processors to: detect one or moreactions attributable to the user; determine a plurality ofcharacteristics related to the user based at least in part upon the oneor more actions attributable to the user, the plurality ofcharacteristics including a level of mindful driving of the user and oneor more additional characteristics of the user; process informationassociated with the level of mindful driving of the user and the one ormore additional characteristics of the user; determine a quality of theuser as a perspective or existing customer based at least in part uponthe level of mindful driving of the user and the one or more additionalcharacteristics of the user; determine a level of carbon offset rewardbased at least in part upon the quality of the user as the perspectiveor existing customer; and offer an amount of carbon offset reward to theuser based at least in part upon the level of carbon offset reward;wherein: the amount of carbon offset reward includes a first amount forplanting one or more first trees at a first time and a second amount forplanting one or more second trees at a second time; each of the one ormore first trees corresponds to a lifespan; the first time precedes thesecond time by a time duration; and the time duration is shorter than orequal to the lifespan.
 9. The computing device of claim 8, wherein: theone or more actions attributable to the user include the user making oneor more vehicle trips; and the instructions that cause the one or moreprocessors to determine the plurality of characteristics related to theuser based at least in part upon the one or more actions attributable tothe user further comprise instructions that cause the one or moreprocessors to: analyze driving data associated with the one or morevehicle trips; and determine the level of mindful driving of the userbased at least in part upon the driving data.
 10. The computing deviceof claim 8, wherein: the one or more actions attributable to the userinclude the user making one or more vehicle trips; and the instructionsthat cause the one or more processors to determine the plurality ofcharacteristics related to the user based at least in part upon the oneor more actions attributable to the user further comprise instructionsthat cause the one or more processors to: analyze driving dataassociated with the one or more vehicle trips; and determine the one ormore additional characteristics of the user based at least in part uponthe driving data, the one or more additional characteristics being theuser reducing a number of the one or more vehicle trips.
 11. Thecomputing device of claim 8, wherein: the one or more actionsattributable to the user include the user making a trip between aparticular pair of origination and destination points; and theinstructions that cause the one or more processors to determine theplurality of characteristics related to the user based at least in partupon the one or more actions attributable to the user further compriseinstructions that cause the one or more processors to: analyze trip dataassociated with the trip between the particular pair of origination anddestination points; and determine the one or more additionalcharacteristics of the user based at least in part upon the trip data,the one or more additional characteristics being the user using analternate form of transportation to make the trip between the particularpair of origination and destination points.
 12. The computing device ofclaim 8, wherein: the one or more actions attributable to the userinclude the user interacting with a mobile device; and the instructionsthat cause the one or more processors to determine the plurality ofcharacteristics related to the user based at least in part upon the oneor more actions attributable to the user further comprise instructionsthat cause the one or more processors to: analyze mobile device dataassociated with the user interacting with the mobile device; anddetermine the one or more additional characteristics of the user basedat least in part upon the mobile device data, the one or more additionalcharacteristics being the user using an application installed on themobile device for a certain number of times.
 13. The computing device ofclaim 8, wherein: the one or more actions attributable to the userinclude the user interacting with a mobile device; and the instructionsthat cause the one or more processors to determine the plurality ofcharacteristics related to the user based at least in part upon the oneor more actions attributable to the user further comprise instructionsthat cause the one or more processors to: analyze mobile device dataassociated with the user interacting with the mobile device; anddetermine the one or more additional characteristics of the user basedat least in part upon the mobile device data, the one or more additionalcharacteristics being the user referring a friend to use an applicationinstalled on the mobile device.
 14. The computing device of claim 8,wherein the instructions that cause the one or more processors todetermine the quality of the user as the perspective or existingcustomer based at least in part upon the level of mindful driving of theuser and the one or more additional characteristics of the user furthercomprise instructions that cause the one or more processors to:determine a probability that the user will become a customer and anestimated revenue from the user if the user becomes the customer; anddetermine an expected business value of the user based at least in partupon the probability that the user will become the customer and theestimated revenue from the user if the user becomes the customer, theexpected business value of the user indicating the quality of the user.15. A non-transitory computer-readable medium storing instructions foroffering a carbon offset reward corresponding to a user, theinstructions when executed by one or more processors of a computingdevice, cause the computing device to: detect one or more actionsattributable to the user; determine a plurality of characteristicsrelated to the user based at least in part upon the one or more actionsattributable to the user, the plurality of characteristics including alevel of mindful driving of the user and one or more additionalcharacteristics of the user; process information associated with thelevel of mindful driving of the user and the one or more additionalcharacteristics of the user; determine a quality of the user as aperspective or existing customer based at least in part upon the levelof mindful driving of the user and the one or more additionalcharacteristics of the user; determine a level of carbon offset rewardbased at least in part upon the quality of the user as the perspectiveor existing customer; and offer an amount of carbon offset reward to theuser based at least in part upon the level of carbon offset reward;wherein: the amount of carbon offset reward includes a first amount forplanting one or more first trees at a first time and a second amount forplanting one or more second trees at a second time; each of the one ormore first trees corresponds to a lifespan; the first time precedes thesecond time by a time duration; and the time duration is shorter than orequal to the lifespan.
 16. The non-transitory computer-readable mediumof claim 15, wherein: the one or more actions attributable to the userinclude the user making one or more vehicle trips; and the instructionswhen executed by the one or more processors that cause the computingdevice to determine the plurality of characteristics related to the userbased at least in part upon the one or more actions attributable to theuser further cause the computing device to: analyze driving dataassociated with the one or more vehicle trips; and determine the levelof mindful driving of the user based at least in part upon the drivingdata.
 17. The non-transitory computer-readable medium of claim 15,wherein: the one or more actions attributable to the user include theuser making one or more vehicle trips; and the instructions whenexecuted by the one or more processors that cause the computing deviceto determine the plurality of characteristics related to the user basedat least in part upon the one or more actions attributable to the userfurther cause the computing device to: analyze driving data associatedwith the one or more vehicle trips; and determine the one or moreadditional characteristics of the user based at least in part upon thedriving data, the one or more additional characteristics being the userreducing a number of the one or more vehicle trips.
 18. Thenon-transitory computer-readable medium of claim 15, wherein: the one ormore actions attributable to the user include the user making a tripbetween a particular pair of origination and destination points; andinstructions when executed by the one or more processors that cause thecomputing device to determine the plurality of characteristics relatedto the user based at least in part upon the one or more actionsattributable to the user further cause the computing device to: analyzetrip data associated with the trip between the particular pair oforigination and destination points; and determine the one or moreadditional characteristics of the user based at least in part upon thetrip data, the one or more additional characteristics being the userusing an alternate form of transportation to make the trip between theparticular pair of origination and destination points.
 19. Thenon-transitory computer-readable medium of claim 15, wherein: the one ormore actions attributable to the user include the user interacting witha mobile device; and the instructions when executed by the one or moreprocessors that cause the computing device to determine the plurality ofcharacteristics related to the user based at least in part upon the oneor more actions attributable to the user further cause the computingdevice to: analyze mobile device data associated with the userinteracting with the mobile device; and determine the one or moreadditional characteristics of the user based at least in part upon themobile device data, the one or more additional characteristics being theuser using an application installed on the mobile device for a certainnumber of times.
 20. The non-transitory computer-readable medium ofclaim 15, wherein the instructions when executed by the one or moreprocessors that cause the computing device to determine the quality ofthe user as the perspective or existing customer based at least in partupon the level of mindful driving of the user and the one or moreadditional characteristics of the user further cause the computingdevice to: determine a probability that the user will become a customerand an estimated revenue from the user if the user becomes the customer;and determine an expected business value of the user based at least inpart upon the probability that the user will become the customer and theestimated revenue from the user if the user becomes the customer, theexpected business value of the user indicating the quality of the user.